Burden of disease - Relative risk

Burden of disease - Absolute risk

About healthiar (1/5)

Figure: healthiar overview

TODO: update names in poster; make internal testing ticked

About healthiar (2/5)

healthiar core family members (functions)

• attribute_health() with either relative and absolute risk
• attribute_lifetable() life table analysis (RR & AR)
• summary_uncertainty() Monte Carlo simulation
• attribute_mod() modify an existing assessment
• compare() two scenarios
• monetize() health impacts
• cba() cost-benefit analysis
• socialize() discovers inequalities in health impacts
• get_mdi() creates the BEST-COST Multidimensional Deprivation Index (MDI)
• get_daly() by adding up YLL & YLD

About healthiar (1/5)

BEST-COST GitHub repository

Let’s check out the BEST-COST GitHub repo and the README file

About healthiar (4/5)

Installation

The README file contains the following information

• GET STARTED WITH THE healthiar R PACKAGE

• Initial installation of the healthiar package from GitHub

• TERMS OF USE

• NOTE

• DISCLAIMER

About healthiar (5/5)

Example of a healthiar function call

healthiar in RStudio 1/3

RStudio startup screen

healthiar in RStudio 2/3

Post installation, you can access the healthiar package landing page in RStudio by going to the Packages tab and then clicking on the healthiar package.

healthiar in RStudio 3/3

Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

Vignette (1/2)

vignette

long-form guide to a package

The intro vignette introduces healthiar step-by-step and contains (reproducible) examples. You can open the intro_to_healthiar vignette within RStudio or as a HTML within your browser.

Function documentation (1/3)

Let’s clarify some R jargon

Function documentation (2/3)

Access the function documentation by clicking on a function name in the package landing page.

Any function documentation contains the following sections:

• Title Essence of the function

• Description What does the function do exactly?

• Usage Bare-minimum examples of how to use the function, including default argument value(s)

• Arguments Short description of each function argument, e.g. input type (numeric vs. string), options (if available), how each argument affects the output, …

• Details (optional) Additional details about the function

• Value Information about the function output

• Examples (optional) Shows how the function works

Function documentation (3/3)

Example: attribute_health() with RR

Goal: attribute COPD cases to air pollution

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear", # Alternatives: "linear", "log_log"
    rr_central = 1.369, 
    rr_increment = 10,  # μg / m^3
    exp_central = 8.85, # μg / m^3
    cutoff_central = 5, # μg / m^3
    bhd_central = 30747 # baseline health data: COPD incidence
  ) 

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = exdat_pm_copd$relative_risk, 
    rr_lower = exdat_pm_copd$relative_risk_lower,
    rr_upper = exdat_pm_copd$relative_risk_upper,
    rr_increment = 10, 
    exp_central = exdat_pm_copd$mean_concentration,
    cutoff_central = exdat_pm_copd$cut_off_value,
    bhd_central = exdat_pm_copd$incidents_per_100_000_per_year/1E5*exdat_pm_copd$population_at_risk,
    # bhd_central = exdat_pm_copd$incidence # Uncomment once change committed to main
  ) 

Output structure

Every attribute output consists of two lists (“folders”)

• health_main contains the main results

• health_detailed detailed results (and in some cases even more information about the assessment/calculation)

TODO: add to detailed bullet: interim results, input; add that tibble works like data frame note: add here that format is tibble (but can be changed to data frame)

note: screenshot of how to click on variable in env (put that as first option) . . .

Different ways to access the results

Let’s check the main output!

results_pm_copd[["health_main"]]

Example: attribute_health() with RR & uncertainty

Goal: attribute lung cancer deaths to PM2.5 exposure

results_pm_copd <-
  healthiar::attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.124, 
    rr_upper = 1.664,
    rr_increment = 10, 
    exp_central = 8.85, 
    exp_lower = 8, 
    exp_upper = 10,
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, 
    bhd_upper = 32000
  ) 

Let’s check the detailed output!

results_pm_copd[["health_detailed"]][["impact_raw"]]

Include summary uncertainty

TODO: add here Monte Carlo example

Iteration example (1/)

Goal: attribute disease cases to PM2.5 exposure in multiple geographic units, such as municipalities, provinces, countries, …

TODO: make printed table smaller, select relevant columns

results_iteration <- healthiar::attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = as.list(c(11, 11, 10, 8, 7)),
    bhd_central = as.list(c(4000, 2500, 3000, 1500, 500))
)

|> as.list()

TODO: remove pipe; replace as.list with list

results_iteration <- healthiar::attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = c(11, 11, 10, 8, 7) |> as.list(),
    bhd_central = c(4000, 2500, 3000, 1500, 500) |> as.list()
)

:::

Let’s check the iteration output! (/)

results_iteration[["health_main"]]

Example: Iteration - Analysis of multiple geo units

Analogously to the single geo unit example the iteration can also be run with uncertainties in one or more input variables.

results_iteration <- healthiar::attribute_health(
    geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"), 
    geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
    rr_central = 1.369, 
    rr_lower = 1.124,
    rr_upper = 1.664,
    rr_increment = 10, 
    cutoff_central = 5,
    erf_shape = "log_linear",
    exp_central = as.list(c(11, 11, 10, 8, 7)),
    exp_lower = as.list(c(10, 10, 9, 7, 6)),
    exp_upper = as.list(c(12, 12, 11, 9, 8)),
    bhd_central = as.list(c(4000, 2500, 3000, 1500, 500)),
    bhd_lower = as.list(c(3000, 1875, 2250, 1125, 375)),
    bhd_upper = as.list(c(5000, 3125, 3750, 1875, 625))
)

results_iteration[["health_detailed"]][["impact_raw"]]

Export results

write.csv(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.csv")

save(results_pm_copd, file = "exported_results/results_pm_copd.Rdata")

openxlsx::write.xlsx(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.xlsx")

Visualize results

Visualization is out of scope of healthiar. You can visualize in

• R, e.g. with the ggplot2 package (online book by the creator)
• Excel (export results first)
• Other tools

Exercise 1 - RR

• How many lung cancer cases are attributable to PM2.5 exposure in a single year?

  • With a cutoff of 5 microgram/m^3 ?

  • With no cutoff?

• What’s the direct treatment costs per year?

Data

• Population-weighted annual mean exposure: 10 microgram/m^3
• Baseline health data: 100’000 cases per year
• Relative risk: 1.06 per 10 microgram/m^3 exposure increment
• ERF shape: linear
• Average treatment cost: 50’000 EURO

Exercise 2 - AR

How many people are highly annoyed due to noise exposure

• In total ?
• In the highest exposure band ?

Advanced How large is the health burden measured in YLD? - Assume that the population is exposed for 1 year - Tip: check the function documentation of attribute_health to see which extra argument must be used to express the burden in YLD

Data

• Use the data in the healthiar example data set exdat_noise_ha
• Use the exposure values from the column exposure_mean
• Use the ERF 90-3.1162*c+0.0342*c^2
• Advanced disability weight = 0.02

Thank you for your attention, and happy coding!

Function documentation

Solution Exercise 1

How many COPD cases are attributable to PM2.5 exposure in a single year?

results_with_cutoff <- attribute_health(
    erf_shape = "linear",
    rr_central = 1.06, 
    rr_increment = 10,  # μg / m^3
    exp_central = 10, # μg / m^3
    cutoff_central = 5, # μg / m^3
    bhd_central = 100000 # baseline health data: COPD incidence
)

# Attributable impact
print(results_with_cutoff$health_main$impact_rounded)

[1] 2913

results_no_cutoff <- attribute_health(
    erf_shape = "linear",
    rr_central = 1.06, 
    rr_increment = 10,
    exp_central = 10,
    cutoff_central = 0,
    bhd_central = 100000
)

# Attributable impact
print(results_no_cutoff$health_main$impact_rounded)

[1] 5660

results_monetized <- monetize(
  output_healthiar = results_no_cutoff,
  valuation = 50000
)

# Cost
print(results_monetized$monetization_main$monetized_impact_rounded)

[1] 283018868

Solution Exercise 2

results_noise_ha <- attribute_health(
    approach_risk = "absolute_risk",
    exp_central = exdat_noise_ha$exposure_mean,
    pop_exp = exdat_noise_ha$population_exposed_total,
    erf_eq_central = "90-3.1162*c+0.0342*c^2"
)

# Total attributable cases of high annoyance
print(results_noise_ha$health_main$impact_rounded)

[1] 278894

results_noise_ha <- attribute_health(
    approach_risk = "absolute_risk",
    exp_central = exdat_noise_ha$exposure_mean,
    pop_exp = exdat_noise_ha$population_exposed_total,
    erf_eq_central = "90-3.1162*c+0.0342*c^2"
)

# Attributable cases of high annoyance from exposure in highest noise band
print(results_noise_ha$health_detailed$impact_raw$impact[5])

[1] 4150.935

results_noise_ha_yld <- attribute_health(
    approach_risk = "absolute_risk",
    exp_central = exdat_noise_ha$exposure_mean,
    pop_exp = exdat_noise_ha$population_exposed_total,
    erf_eq_central = "90-3.1162*c+0.0342*c^2",
    dw_central = 0.02,
    duration_central = 1
)

# Attributable number of YLD due to high annoyance
print(results_noise_ha_yld$health_main$impact_rounded)

[1] 5578